Model Predictive Control (MPC)-Based Supervisory Control and Design of Off-Gas Recovery Plant with Periodic Disturbances from Parallel Batch Reactors

Process Drive Sizing Methodology and Multi-Level Modeling Linking MATLAB® and Aspen Plus® Environment

Optimal steam process drive sizing is crucial for efficient and sustainable operation of energy-intense industries. Recent years have brought several methods assessing this problem, which differ in complexity and user-friendliness. In this paper, a novel complex method was developed and presented and its superiority over other approaches was documented on an industrial case study. Both the process-side and steam-side characteristics were analyzed to obtain correct model input data: Driven equipment performance and efficiency maps were considered, off-design and seasonal operation was studied, and steam network topology was included. Operational data processing and sizing calculations were performed in a linked MATLAB®–Aspen Plus® environment, exploiting the strong sides of both software tools. The case study aimed to replace a condensing steam turbine by a backpressure one, revealing that: 1. Simpler methods neglecting frictional pressure losses and off-design turbine operation efficiency loss undersized the drive and led to unacceptable loss of deliverable power to the process; 2. the associated process production loss amounted up to 20%; 3. existing bottlenecks in refinery steam pipelines operation were removed; however, new ones were created; and 4. the effect on the marginal steam source operation may vary seasonally. These findings accentuate the value and viability of the presented method.

Development of mixed constrained RTDA controller for industrial applications

Most of the industrial processes are MIMO and constrained in nature. So the development of MIMO constrained controller is always preferable and makes challenges to the control community. This paper proposes the development of constrained next generation RTDA controller to handle various linear inequality constraints in a multi-variable control framework. The Lagrangian function is formulated, and the dual problem is solved using Hildreth's algorithm. The combined hard-soft constrained RTDA optimization problem with exact penalty functions is solved using KKT conditions. Performance of the developed constrained RTDA controller is demonstrated using simulation studies of typical industrial unit operations such as nonlinear CSTR process and Wood and Berry distillation column. The performance of the constrained RTDA controller is compared with constrained DMC and GPC.